PhD Student in Physics-Informed Graph Neural Networks for Wind Turbine Health Monitoring — EPFL
- Location
- Lausanne
- Contract
- full-time
- Posted
- 48 days ago
Role overview
IMOS The Intelligent Maintenance and Operations Systems (IMOS) Lab at EPFL is looking for a motivated and out-of-the-box thinking PhD researcher, (100%, in Lausanne, fixed-term) starting in September or upon agreement.
Project description The objective of this project is to develop novel methodologies based on physics-informed graph neural networks (PI-GNNs) to understand and model the impact of operational loads on system degradation at the compenent level in complex engineering systems, with a particular focus on wind turbines.
The research will focus on explicitly integrating physical laws, load dynamics, and degradation mechanisms into graph-based models, enabling a principled understanding of how operating conditions drive the evolution of system health over time.
- IMOS The Intelligent Maintenance and Operations Systems (IMOS) Lab at EPFL is looking for a motivated and out-of-the-box thinking PhD researcher, (100%, in Lausanne, fixed-term) starting in September or upon agreement.
- Project description The objective of this project is to develop novel methodologies based on physics-informed graph neural networks (PI-GNNs) to understand and model the impact of operational loads on system degradation at the compenent level in complex engineering systems, with a particular focus on wind turbines.
Application process
- Applications will include complex industrial and energy systems, with a particular focus on wind turbines, where load conditions directly influence the degradation of critical components such as blades, gearboxes, and bearings.
- The developed methods will contribute to improving lifetime modeling, reliability assessment, and physics-informed predictive maintenance.
- This PhD position is part of an ERC Consolidator Grant, supporting cutting-edge research on physics-informed AI, intelligent maintenance, and the modeling of degradation processes in complex systems.
- Profile We are looking for a PhD candidate with a strong analytical background and an outstanding MSc degree in Mechanical Engineering, Computational Mechanics, Engineering Science, Physics, Applied Mathematics, or a closely related field.
- You should have a solid foundation in machine learning (e.g., deep learning) and mathematical modeling, including experience with dynamical systems or differential equations.
- A strong interest in modeling physical systems and degradation processes (e.g., fatigue, damage accumulation) is expected.
- Experience with graph neural networks or spatiotemporal models is highly desirable, as well as familiarity with physics-informed approaches that incorporate physical inductive bias into learning models.
- Knowledge of one or more of the following areas is considered a strong asset:
